Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/229
Title: A multiple maximum scatter difference discriminant criterion for facial feature extraction
Authors: Song, Fengxi
Zhang, David D.
Mei, Dayong
Guo, Zhongwei
Subjects: Face recognition
Feature extraction
Linear discriminant criterion
Issue Date: Dec-2007
Publisher: IEEE
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, Dec. 2007, v. 37, no. 6, p.1599-1606.
Abstract: Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart—multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature- extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD outperforms state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.
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Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/229
DOI: 10.1109/TSMCB.2007.906579
ISSN: 1083-4419
Appears in Collections:COMP Journal/Magazine Articles

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